Price discrimination with inequity-averse consumers: A reinforcement learning approach

Katrin Buchali

No 02-2021, Hohenheim Discussion Papers in Business, Economics and Social Sciences from University of Hohenheim, Faculty of Business, Economics and Social Sciences

Abstract: With the advent of big data, unique opportunities arise for data collection and analysis and thus for personalized pricing. We simulate a self-learning algorithm setting personalized prices based on additional information about consumer sensitivities in order to analyze market outcomes for consumers who have a preference for fair, equitable outcomes. For this purpose, we compare a situation that does not consider fairness to a situation in which we allow for inequity-averse consumers. We show that the algorithm learns to charge different, revenue-maximizing prices and simultaneously increase fairness in terms of a more homogeneous distribution of prices.

Keywords: pricing algorithm; reinforcement learning; Q-learning; price discrimi-nation; fairness; inequity (search for similar items in EconPapers)
JEL-codes: D63 D91 L12 (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-big, nep-cmp, nep-com, nep-ind and nep-mkt
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:hohdps:022021

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